on 23-Sep-2022 (Fri)

The oesophagus is a muscular tube, approximately 25 cm long, mainly occupying the posterior mediastinum and extending from the upper oesophageal sphincter (the cricopharyngeus muscle) in the neck to the junction with the cardia of the stomach.

The parasym- pathetic nerve supply is mediated by branches of the vagus nerve that has synaptic connections to the myenteric (Auer- bach’s) plexus. Meissner’s submucosal plexus is sparse in the oesophagus.

The body of the oesophagus propels the bolus through a relaxed lower oesophageal sphincter (LOS) into the stomach, taking air with it (Figure 62.2). This coordi- nated oesophageal wave which follows a conscious swallow is called primary peristalsis. It is under vagal control, although there are specific neurotransmitters that control the LOS.

The upper oesophageal sphincter is normally closed at rest and serves as a protective mechanism against regurgita- tion of oesophageal contents into the respiratory passages. It also serves to stop air entering the oesophagus other than the small amount that enters during swallowing

Secondary peristalsis is the normal reflex preceded by a conscious swallow. It is worth remembering that most clear- ance swallows to neutralise refluxed gastric acid are, however, achieved by primary peristalsis, which carries saliva with its high bicarbonate content down to the lower oesophagus.

Tertiary contractions are non-peristaltic waves that are infre- quent

Oesophageal dys- phagia occurs in the involuntary phase and is characterised by a sensation of food sticking

When there is a problem with swallowing in the voluntary (oral or pharyngeal) phases, patients will usually say that they cannot swallow properly, but they do not characteris- tically describe ‘food sticking’. Instead, when they try to ini- tiate a conscious swallow, food fails to enter the oesophagus, stays in the mouth or enters the airway, causing coughing or spluttering. Virtually all causes of this type of dysphagia are chronic neurological or muscular diseases.

Regurgitation should strictly refer to the return of oesophageal contents from above a functional or mechanical obstruction. Reflux is the passive return of gas- troduodenal contents to the mouth as part of the symptom- atology of gastro-oesophageal reflux disease (GORD).

Contrast radiography has been somewhat overshadowed by endoscopy but remains a useful investigation to demonstrate changes in oesophageal diameter, anatomical distortion or abnormal motility.

Magnification is a standard feature of the mod- ern endoscope and widely used in conjunction with agents that can be sprayed on to the mucosa, such as acetic acid to enhance mucosal detail. Novel techniques that rely on fluo- rescence and narrow band imaging to enhance visual contrast are becoming increasingly used for the identification of muco- sal abnormalities that are not easily seen with white light, e.g. in patients with Barrett’s oesophagus undergoing endoscopic surveillance.

Endoscopic ultrasonography relies on a high-frequency (5–30 MHz) transducer

Prolonged measurement of pH is now accepted as the most accurate method for the diagnosis of gastro-oesophageal reflux. It is particularly useful in patients with atypical reflux symptoms, those without endoscopic oesophagitis and when patients respond poorly to intensive medical therapy

Most laboratories use a scoring system (Johnson–DeMeester) to create a numerical value

. An oesopha- geal pH <.

To restore normal swallowing, a stricture should be dilated to at least 16 mm in diameter, although this may need to be achieved over a series of procedures, depending on the perceived nature of the stricture.

Perforation of the oesophagus is usually iatrogenic (at thera- peutic endoscopy) or due to ‘barotrauma’ (spontaneous perforation).

Barotrauma (spontaneous perforation, Boerhaave’s syndrome) This occurs classically when a person vomits against a closed glottis. The pressure in the oesophagus increases rapidly, and the oesophagus bursts at its weakest point in the lower third, sending a stream of material into the mediastinum and often the pleural cavity as well.

What could be more delightful than to have in the same few minutes all the fascinating terrors of going abroad combined with all the humane security of coming home again? What could be better than to have all the fun of discovering South Africa without the disgusting necessity of landing there? What could be more glorious than to brace one's self up to discover New South Wales and then realize, with a gush of happy tears, that it was really old South Wales. This at least seems to me the main problem for philosophers, and is in a manner the main problem of this book. How can we contrive to be at once astonished at the world and yet at home in it?

A small circle is quite as infinite as a large circle; but, though it is quite as infinite, it is not so large. In the same way the insane explanation is quite as complete as the sane one, but it is not so large. A bullet is quite as round as the world, but it is not the world.

Results Optimal training difficulty for binary classification tasks.Ina standard binary classification task, an animal or machine ‘agent’ makes binary decisions about simple stimuli. For example, in the classic Random Dot Motion paradigm from Psychology and Neuroscience15,16, stimuli consist of a patch of moving dots— most moving randomly but a small fraction moving coherently either to the left or the right—and participants must decide in which direction the coherent dots are moving. A major factor in determining the difficulty of this perceptual decision is the frac- tion of coherently moving dots, which can be manipulated by the experimenter to achieve a fixed error rate during training using a procedure known as ‘staircasing’17. We assume that agents make their decision on the basis of a scalar, subjective decision variable, h, which is computed from a stimulus that can be represented as a vector x(e.g., the direction of motion of all dots) h¼Φðx;ϕÞð1Þ where Φ(⋅) is a function of the stimulus and (tunable) parameters ϕ. We assume that this transformation of stimulus xinto the subjective decision variable hyields a noisy representation of the true decision variable, Δ(e.g., the fraction of dots moving left). That is, we write h¼Δþnð2Þ where the noise, n, arises due to the imperfect representation of the decision variable. We further assume that this noise, n,is random and sampled from a zero-mean Gaussian distribution with standard deviation σ(Fig. 1a). If the decision boundary is set to 0, such that the model chooses option A when h> 0, option B when h< 0 and randomly when h=0, then the noise in the representation of the decision variable leads to errors with probability ER ¼Z0 1 pðhjΔ;σÞdh ¼Fð Δ=σÞ¼Fð βΔÞð3Þ where F(x) is the cumulative density function of the standardized noise distribution, p(x)=p(x|0, 1), and β=1/σquantifies the precision of the representation of Δand the agent’s skill at the task. As shown in Fig. 1b, this error rate decreases as the decision gets easier (Δincreases) and as the agent becomes more accomplished at the task (βincreases). The goal of learning is to tune the parameters ϕsuch that the subjective decision variable, h, is a better reflection of the true decision variable, Δ. That is, the model should aim to adjust the parameters ϕso as to decrease the magnitude of the noise σor, equivalently, increase the precision β. One way to achieve this tuning is to adjust the parameters using gradient descent on the error rate, i.e. changing the parameters over time taccording to dϕ dt ¼ η∇ϕER ð4Þ where ηis the learning rate and ∇ϕER is the derivative of the error rate with respect to parameters ϕ. This gradient can be

written in terms of the precision, β,as ∇ϕER ¼∂ER ∂β∇ϕβð5Þ Note here that only the first term on the right hand side of Eq. (5) depends on the difficulty Δ, while the second describes how the precision changes with ϕ. Note also that Δitself, as the ‘true’ decision variable, is independent of ϕ. This means that the optimal difficulty for training, that maximizes the change in the parameters, ϕ,at this time point, is the value of the decision variable Δ*that maximizes ∂ER/∂β. Of course, this analysis ignores the effect of changing ϕon the form of the noise—instead assuming that it only changes the scale factor, β, an assumption that likely holds in the relatively simple cases we consider here, although whether it holds in more complex cases will be an important question for future work. In terms of the decision variable, the optimal difficulty changes as a function of precision (Fig. 1c) meaning that the difficulty of training must be adjusted online according to the skill of the agent. Using the monotonic relationship between Δand ER (Fig. 1b) it is possible to express the optimal difficulty in terms of the error rate, ER*(Fig. 1d). Expressed this way, the optimal difficulty is constant as a function of precision, meaning that optimal learning can be achieved by clamping the error rate during training at a fixed value, which, for Gaussian noise is ER ¼1 21 erf 1 ffiffiffi 2 p 0:1587 ð6Þ That is, the optimal error rate for learning is 15.87%, and the optimal accuracy is around 85%. We call this the Eighty Five Percent Rule for optimal learning